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Bioinformatics Vol. 19 no. 15 2003
Pages 1964-1971
© 2003 Oxford University Press

Sequence alignment kernel for recognition of promoter regions

Leo Gordon 1,*, Alexey Ya. Chervonenkis 1,2, Alex J. Gammerman 1, Ilham A. Shahmuradov 1 and Victor V. Solovyev 3

1 Department of Computer Science, Royal Holloway, University of London, Egham, Surrey TW20 0EX, UK, 2 Institute of Control Science, Profsoyuznaja 65, Moscow, Russia and 3 Softberry Inc., 116 Radio Circle, Suite 400, Mount Kisco, NY, 10549, USA

Received on December 6, 2002 ; revised on March 2, 2003 ; accepted on April 29, 2003

In this paper we propose a new method for recognition of prokaryotic promoter regions with startpoints of transcription. The method is based on Sequence Alignment Kernel, a function reflecting the quantitative measure of match between two sequences. This kernel function is further used in Dual SVM, which performs the recognition.

Several recognition methods have been trained and tested on positive data set, consisting of 669 {sigma}70-promoter regions with known transcription startpoints of Escherichia coli and two negative data sets of 709 examples each, taken from coding and non-coding regions of the same genome. The results show that our method performs well and achieves 16.5% average error rate on positive & coding negative data and 18.6% average error rate on positive & non-coding negative data.

Availability:The demo version of our method is accessible from our website http://mendel.cs.rhul.ac.uk/

Contact: leo{at}cs.rhul.ac.uk

* To whom correspondence should be addressed.


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